水平机扫雷达的副瓣干扰对消算法研究

采用采样矩阵逆算法(SMI)对消副瓣干扰,一般抽取部分样本对消整段数据的干扰。当雷达阵面处于旋转扫描状态时,由于角度的变化,该对消性能下降很快。该文提出了采用递归最小二乘算法(RLS)对剩余数据进行跟踪收敛的方法,SMI RLS算法收敛速度快,且逐点样本迭代,能够跟踪干扰相对于阵面角度的变化,从而保证了对消比。同时给出了降低RLS算法计算量的方法。仿真表明,间歇算法在牺牲部分对消性能的前提下,可有效降低计算量。     

基于IOC-CSMP的OFDM系统稀疏信道快速重构算法

针对信道路径数量未知时正交频分复用（OFDM）系统信道估计问题,提出了一种基于内积运算优化与稀疏度更新约束的压缩采样匹配追踪快速重构算法。通过构建与更新选择向量,利用与选择向量中非零值索引对应的原子向量参与内积运算来降低运算量;基于压缩采样与回溯策略来优化原子,利用匹配追踪完成信道估计,通过相邻两次信道估计值的能量差来更新稀疏度并约束算法停止,保证算法快速收敛。仿真结果表明,所提算法具有比最小二乘、最小均方差、稀疏度自适应匹配追踪和自适应正则化压缩采样匹配追踪算法更好的信道估计性能,且比2种自适应方法消耗更少的信道估计时间。     

基于压缩感知和最小二乘的分布式协作频谱感知

针对认知无线电(CR)集中式频谱感知算法对融合中心要求高,而且对节点失效的容忍性也不高等缺点,提出了一种基于压缩感知的分布式多节点协作算法.认知无线电网络中每个CR节点在接收信号频谱后,首先根据压缩采样理论在本地获取压缩采样测量值,然后利用l1范数约束的最小二乘算法在本节点估计频谱,把在此节点估计的频谱传给下一相邻节点,以此进行迭代优化直到算法收敛.理论分析和仿真结果表明,所提算法不仅计算复杂度低,收敛速度快,而且精确重构性能好,可靠性较高.     

基于递归最小二乘的DS-SS系统抗窄带干扰技术

该文针对DS-SS系统的未知窄带干扰,通过分析接收信号统计特性,提出递归最小二乘(RLS)滤波的干扰抑制算法。与盲递推最小二乘算法相比,减少了约束条件,降低了复杂度,而且克服了LS滤波算法不能实时处理的弱点,可以实现快速算法。仿真结果表明,该算法抑制干扰对DS-SS系统性能改善优于自适应线性滤波算法,而且不用考虑算法的收敛性,更利于工程应用。     

基于混沌压缩感知的稀疏时变信号在线估计

混沌压缩感知是一种利用混沌系统实现非线性测量的压缩感知理论。针对稀疏时变信号的混沌压缩感知,该文提出稀疏时变信号的在线估计架构,构建一种递归最小二乘准则下的稀疏约束目标函数;通过利用迭代加权非线性最小二乘算法求解目标函数最小化问题,实现稀疏时变信号的参数估计。以Henon混沌系统为例仿真分析了频域时变稀疏信号的估计性能,数值模拟证明了该方法的有效性。     

基于广义能量函数的扩频码序列盲估计算法

本文提出一种基于广义能量函数(GEF)的直接序列扩频(DS/SS)信号扩频码序列的盲估计方法.广义能量函数通过引入一个加权矩阵来优化线性神经网络的连接权矢量,可以推导出一种新的递归最小二乘(RLS)学习算法.该算法能高效提取一个输入信号相关矩阵的多个主分量,可对同步和非同步模型下的PN码序列实现盲估计.该算法具有收敛快、稳健性好等优点,其运算量和储存量远远小于特征值分解算法,收敛速度、相关性能和运算复杂度等恢复性能优于压缩投影逼近子空间跟踪(PASTd)算法和改进神经网络(MHR)算法.计算机仿真证明,该算法能在较低的信噪比条件下,实时高效地恢复PN码序列,具有优异的性能.     

一种高效快速的二相码旁瓣抑制算法

高效的旁瓣抑制能力可以提高雷达对微弱目标的检测能力,传统的旁瓣抑制方法中,递归最小二乘算法（RLS）不仅数值稳定性差,而且运算量大。通过合理构建脉冲压缩后的信号模型,提出一种基于横向滤波器的快速旁瓣抑制算法。该算法采用投影技术和向量空间法,不仅大大减小了运算量,而且具有高效的旁瓣抑制特性。仿真实验表明：该算法迭代2次就可收敛于真值,迭代5次即可达到RLS算法迭代100次的旁瓣抑制效果,并且信噪比损失要低于RLS算法。     

MIMO-OFDM系统中基于迭代MAP算法的RLS信道估计

该文提出了一种适用于MIMO-OFDM系统的迭代最大后验概率(Iterative-MAP)信道估计算法。接收机利用MAP译码算法中的信息位和校验位软信息,经过非线性映射将信息反馈至信道估计模块,采用递归最小二乘(RLS)自适应滤波算法对信道时变状态参数进行跟踪,提高了信道估计的精度。仿真结果表明,该方法与最小二乘(LS)算法相比,估计的均方误差(MSE)和误帧率(FER)性能都有较大改善。     

平方根递归最小二乘自适应判决反馈均衡器算法

在近四年来发展的平方根递归最小二乘(RLS)算法的基础上,本文导出了一种新形式的自适应均衡器算法——分数间隔(Fractional]y-spaced)平方根RLS判决反馈均衡(DFE)算法。除了保持DFE的优良特性外,该算法以少量计算量增加的代价改善了普通RLS均衡算法(如快速Kalman、斜格算法等)的数值稳定性并压缩了其动态范围。文中给出了数值结果对比。     

MC-CDMA系统中一种线性共轭MOE多用户检测算法

该文提出MC-CDMA系统下一种基于递归最小二乘(Recursive Least-Squares, RLS)的最小输出能量(Minimum Output Energy, MOE)噪声抑制线性共轭多用户检测算法.该算法定义了一种新的基于MOE准则的代价函数,同时将噪声子空间作为MOE代价函数的约束条件,设计了一种噪声抑制的线性共轭检测器,并采用RLS算法自适应得到权向量.所提算法将权向量和噪声子空间正交,消除了权向量中的噪声分量,并且利用了伪自相关矩阵的信息,从而提高了系统的性能.仿真结果证明了本文算法的有效性和优越性.     

Low Complexity Linear Channel Estimation for MIMO Communication Systems

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

The multi-user detection under fading channel

This paper proposes a method of blind multi-user detection algorithm based on signal sub-space estimation under the fading channels in the present of impulse noise. This algorithm adapts recursive least square (RLS) filter that can estimate the coefficients using only the signature waveform. In addition, to strengthen the ability of resisting the impulse noise, a new suppressive factor is induced, which can suppress the amplitude of the impulse, and improve the ability of convergence speed. Simulation results show that new RLS algorithm is more robust against consecutive impulse noise and have better convergence ability than conventional RLS. In addition, Compared to the least mean square (LMS) detector, the new robust RLS sub-space based method has better multi-address-inference (MAI) suppressing performance, especially, when channel degrades.     

A new system identification method for fast echo cancelerinitialization

Proposes a new recursive version of an earlier technique for fast initialization of data-driven echo cancelers (DDECs). The speed of convergence and the covariance of the estimate of the proposed technique are comparable to the recursive least squares (RLS) algorithm, however, the computational complexity is no greater than the least mean square (LMS) algorithm. Analysis of computational complexity and the estimation error is also provided. Simulation results based on both floating-point and fixed-point arithmetic illustrate a remarkable improvement in terms of speed of convergence and steady-state error over the computationally comparable LMS algorithm     

Acquisition performance of an adaptive receiver for DS-CDMA

This paper presents a single-user code timing estimation algorithm for direct-sequence code-division multiple access that is based on processing the weight vector of an adaptive filter. The filter weight vector can be shown to adapt in the mean to a scaled time-shifted version of the spreading code of the desired user. Therefore, our algorithm requires very little side information in order to form its estimate. The acquisition performance of the algorithm is investigated when the filter is adapted using the least mean square (LMS) or the recursive least square (RLS) algorithm. The proposed algorithm is shown through experimental results to be resistant to the near-far problem when the RLS adaptation algorithm is used, but not when the LMS algorithm is used. However, the performance of this code-acquisition technique is still substantially better than the traditional correlator-based approach, even when the computationally simple LMS algorithm is used. As an extension to the basic timing estimator algorithm, we consider the effect of frequency synchronization error on the performance of the timing estimate. As expected, frequency-offset error degrades the performance of the timing estimate. However, a modified version of the adaptive filter is presented to combat this effect     

EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods     

CS quadrilinear model-based angle estimation for MIMO radar with electromagnetic vector sensors

In order to estimate the angles for bistatic MIMO radar with electromagnetic vector sensors, we link the compressed sensing (CS) theory with quadrilinear model, and propose a novel angle estimation algorithm. In the proposed algorithm, the received data is firstly arranged into a quadrilinear model and then it is compressed according to the compressed sensing theory. We then perform quadrilinear decomposition on the compressed quadrilinear data model via the quadrilinear alternating least square (QALS) algorithm and finally obtain the automatically paired angle estimates with sparsity. Owing to compression, the proposed algorithm has smaller storage requirement and lower computational complexity than the conventional quadrilinear decomposition-based algorithm. Moreover, our algorithm has higher angle estimation accuracy than the estimation signal parameters via rotational invariance techniques (ESPRIT) algorithm and its estimation performance is close to that of the conventional quadrilinear decomposition-based algorithm. Our proposed algorithm needs neither additional pair matching, nor spectral peak searching, and it can be applied to both uniform and non-uniform arrays. Effectiveness of our proposed algorithm is assessed through various simulation results.     

自适应序贯M估计算法及其性能分析

针对复杂噪声环境下的参数估计问题,提出了一种稳健的自适应序贯M估计算法(Adaptive Recursive M-Estimation,ARME),并从理论分析和Monte Carlo实验仿真两方面分析了该算法的收敛性、渐进无偏特性和稳健性.理论分析和仿真试验表明:在高斯白噪声背景下,ARME具有与序贯最小二乘算法(Recursive Least Square,RLS)相近的性能;在有突出干扰等非高斯噪声背景下,与RLS相比,ARME的参数估计收敛速度更快,估计误差更小,而且在稳健性上大大优于RLS.     

Convergence Analysis of the Hierarchical Least Squares Algorithm for Bilinear-in-Parameter Systems

This paper studies the convergence of the hierarchical identification algorithm for bilinear-in-parameter systems. By replacing the unknown variables in the information vector with their estimates, a hierarchical least squares algorithm is derived based on the model decomposition. The proposed algorithm has higher computational efficiency than the over-parameterization model-based recursive least squares algorithm. The performance analysis shows that the parameter estimation errors converge to zero under persistent excitation conditions. The effectiveness of the proposed algorithm is verified by simulation examples.     

多传感器误差配准研究

多传感器组网信息融合时,需要对组网中各传感器系统误差进行估计和补偿,以消除各传感器系统误差对融合性能的影响.研究了雷达组网系统误差配准模型,并对最小二乘算法(LS)、广义最小二乘算法(GLS)、递推最小二乘算法(RLS)、修正EX算法等误差配准算法进行对比分析,同时给出了扩维配准模型用于解决多传感器组网配准问题,针对实...